Despite substantial investments in high-frequency, remote-sensed forest monitoring in the Amazon, early deforestation alerts generated by these systems rarely reach the most directly affected populations in time to deter deforestation. We study a community monitoring program that facilitated transfer of early deforestation alerts from the Global Forest Watch network to indigenous communities in the Peruvian Amazon and trained and incentivized community members to patrol forests in response to those alerts. The program was randomly assigned to 39 of 76 communities. The results from our analysis suggest that the program reduced tree cover loss, but the estimated effects from the experiment are imprecise: We estimate a reduction of 8.4 ha per community in the first year (95% CI [−19.4, 2.6]) and 3.3 ha in the second year (95% CI: [−13.6, 7.0]) of monitoring. The estimated reductions were largest in communities facing the largest threats. Data from monitoring records and community surveys provide evidence about how the program may affect forest outcomes. Community members perceived that the program’s monitors were new authorities with influence over forest management and that the monitors’ incentivized patrols were substitutes for traditional, unincentivized citizen patrols that suffer from free riding and inhibit timely community detection of and responses to deforestation. Should our findings be replicated elsewhere, they imply that externally facilitated community-based monitoring protocols that combine remote-sensed early deforestation alerts with training and incentives for monitors could contribute to sustainable forest management.
|Original language||English (US)|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Jul 20 2021|
- Collective action
- Common pool resources
- Community monitoring
ASJC Scopus subject areas